Modeling and analysis of the role of fast-response energy storage in the smart grid

Su, Han-I; Gamal, Abbas El

doi:10.1109/allerton.2011.6120239

Cited by 72 publications

(51 citation statements)

References 14 publications

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“…We show that this analytic result corroborates extremely well with the numerical results using the NREL dataset. In [12], we reported preliminary results assuming iid zero-mean Laplace distributed power imbalance and considered only the negative residual power imbalance in the cost function. This paper generalizes and extends the results in [12] in several directions.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing

Gamal

2013

IEEE Trans. Power Syst.

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149

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Abstract-The high variability of renewable energy is a major obstacle toward its increased penetration. Energy storage can help reduce the power imbalance due to the mismatch between the available renewable power and the load. How much can storage reduce this power imbalance? How much storage is needed to achieve this reduction? This paper presents a simple analytic model that leads to some answers to these questions. Considering the multitimescale grid operation, we formulate the power imbalance problem for each timescale as an infinite horizon stochastic control problem and show that a greedy policy minimizes the average magnitude of the residual power imbalance. Observing from the wind power data that in shorter timescales the power imbalance can be modeled as an iid zero-mean Laplace distributed process, we obtain closed form expressions for the minimum cost and the stationary distribution of the stored power. We show that most of the reduction in the power imbalance can be achieved with relatively small storage capacity. In longer timescales, the correlation in the power imbalance cannot be ignored. As such, we relax the iid assumption to a weakly dependent stationary process and quantify the limit on the minimum cost for arbitrarily large storage capacity.

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Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing

Gamal

2013

IEEE Trans. Power Syst.

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149

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“…Different types of energy storage technologies with various response times can serve power system requirements in different time scales [3]. Thus, over a short time span (i.e., seconds to minutes), fast-response energy storage systems can offset the short-term fluctuations in demand and intermittent renewable power generation, and thereby improve the dynamic performance of the power systems.…”

Section: Introductionmentioning

confidence: 99%

Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

Moazeni

Powell

Hajimiragha³

2015

IEEE Trans. Power Syst.

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Abstract-This paper addresses the formulation and solution of an optimal energy storage management problem under risk consideration and transaction costs of trading energy with the power grid. The price evolves as a stochastic process, capable of correctly explaining the seasonality effects as well as the tail fatness and spikiness in its distribution. Transaction costs capture the price impact of the storage operation on the electricity spot price. A risk analysis of an optimal risk neutral deterministic policy as well as the simple myopic policy indicates that the realized operational cost may notably differ from the expected cost by a considerable probability. This difference suggests that we need to consider risk. Using the downside risk measure of Conditional Value-at-Risk, an optimal risk averse conversion and transmission strategy, among the grid, the renewable power generation source, and an energy storage is proposed to fully satisfy the electricity demand and minimize the expected operational cost as well as the risk. Our numerical study using data from NYISO demonstrates the impacts of risk consideration and the transaction cost parameters on the optimal strategy structure, its expected cost, and its risk.

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“…In the first category, consumers are assumed to be price-taking, meaning that they do not consider how their consumption will affect the prices. In this case, the decision-making of a single foresighted consumer is formulated as a stochastic control problem aiming to maximize its long-term utility [11][12][13]. Alternatively, in [15,16], multiple myopic consumers aim to maximize their utility, and their decisions are formulated as static optimization problems among cooperative users.…”

Section: Related Workmentioning

confidence: 99%

“…Recent works [3,[9][10][11][12][13][14] considered consumers' discomfort costs and aimed to jointly minimize the consumers' billing and discomfort costs, by assuming some utility functions. These works can be classified into two categories.…”

Section: Related Workmentioning

confidence: 99%

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Incentive-compatible demand-side management for smart grids based on review strategies

Schaar

2015

EURASIP J. Adv. Signal Process.

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Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system's as well as the users' performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

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Modeling and analysis of the role of fast-response energy storage in the smart grid

Cited by 72 publications

References 14 publications

Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing

Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing

Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

Incentive-compatible demand-side management for smart grids based on review strategies

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